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Visual Programming (VP) has emerged as a powerful framework for Visual Question Answering (VQA).
Andreas, J., Rohrbach, M., Darrell, T., Klein, D.: Neural module networks. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) pp. 39–48 (2015), https://api.semanticscholar.org/CorpusID:5276660
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Antol, S., Agrawal, A., Lu, J., Mitchell, M., Batra, D., Zitnick, C.L., Parikh, D.: Vqa: Visual question answering. In: Proceedings of the IEEE international conference on computer vision. pp. 2425–2433 (2015)
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Hu, R., Andreas, J., Rohrbach, M., Darrell, T., Saenko, K.: Learning to reason: End-to-end module networks for visual question answering. 2017 IEEE International Conference on Computer Vision (ICCV) pp. 804–813 (2017), https://api.semanticscholar.org/CorpusID:18682
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Hudson, D.A., Manning, C.D.: Gqa: A new dataset for real-world visual reasoning and compositional question answering. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) pp. 6693–6702 (2019), https://api.semanticscholar.org/CorpusID:152282269
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Hudson, D.A., Manning, C.D.: Gqa: A new dataset for real-world visual reasoning and compositional question answering. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) pp. 6693–6702 (2019), https://api.semanticscholar.org/CorpusID:152282269
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Hudson, D.A., Manning, C.D.: Learning by abstraction: The neural state machine. In: Neural Information Processing Systems (2019), https://api.semanticscholar.org/CorpusID:195847902
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Marino, K., Rastegari, M., Farhadi, A., Mottaghi, R.: Ok-vqa: A visual question answering benchmark requiring external knowledge. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) pp. 3190–3199 (2019), https://api.semanticscholar.org/CorpusID:173991173
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Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: Language models are few-shot learners. In: Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M., Lin, H. (eds.) Advances in Neural Information Processing Systems. vol. 33, pp. 1877–1901. Curran Associates, Inc. (2020), https://proceedings.neurips.cc/paper/2020/file/1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf
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Subramanian, S., Bogin, B., Gupta, N., Wolfson, T., Singh, S., Berant, J., Gardner, M.: Obtaining faithful interpretations from compositional neural networks. In: Annual Meeting of the Association for Computational Linguistics (2020), https://api.semanticscholar.org/CorpusID:218487535
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2021
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2021
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Kim, W., Son, B., Kim, I.: Vilt: Vision-and-language transformer without convolution or region supervision. In: International Conference on Machine Learning (2021), https://api.semanticscholar.org/CorpusID:231839613
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Xiao, J., Shang, X., Yao, A., Chua, T.S.: Next-qa: Next phase of question-answering to explaining temporal actions. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 9777–9786 (June 2021)
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Zhang, P., Li, X., Hu, X., Yang, J., Zhang, L., Wang, L., Choi, Y., Gao, J.: Vinvl: Revisiting visual representations in vision-language models. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) pp. 5575–5584 (2021), https://api.semanticscholar.org/CorpusID:235692795
2021
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Buch, S., Eyzaguirre, C., Gaidon, A., Wu, J., Fei-Fei, L., Niebles, J.C.: Revisiting the “Video” in Video-Language Understanding. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2022)
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Gupta, T., Kembhavi, A.: Visual programming: Compositional visual reasoning without training. 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) pp. 14953–14962 (2022), https://api.semanticscholar.org/CorpusID:253734854
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Cho, J., Zala, A., Bansal, M.: Visual programming for text-to-image generation and evaluation. NeurIPS (2023)
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Liu, N.F., Lin, K., Hewitt, J., Paranjape, A., Bevilacqua, M., Petroni, F., Liang, P.: Lost in the middle: How language models use long contexts (2023)
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Ma, Y.J., Liang, W., Wang, G., Huang, D.A., Bastani, O., Jayaraman, D., Zhu, Y., Fan, L., Anandkumar, A.: Eureka: Human-level reward design via coding large language models. arXiv preprint arXiv: Arxiv-2310.12931 (2023)
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Liu, F., Emerson, G.E.T., Collier, N.: Visual spatial reasoning. Transactions of the Association for Computational Linguistics 11
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Madaan, A., Zhou, S., Alon, U., Yang, Y., Neubig, G.: Language models of code are few-shot commonsense learners. In: Goldberg, Y., Kozareva, Z., Zhang, Y. (eds.) Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. pp. 1384–1403. Association for Computational Linguistics, Abu Dhabi, United Arab Emirates (Dec 2022). https://doi.org/10.18653/v1/2022.emnlp-main.90, https://aclanthology.org/2022.emnlp-main.90
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Wang, Z., Li, M., Xu, R., Zhou, L., Lei, J., Lin, X., Wang, S., Yang, Z., Zhu, C., Hoiem, D., et al.: Language models with image descriptors are strong few-shot video-language learners. Advances in Neural Information Processing Systems 35
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Subramanian, S., Narasimhan, M., Khangaonkar, K., Yang, K., Nagrani, A., Schmid, C., Zeng, A., Darrell, T., Klein, D.: Modular visual question answering via code generation. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). pp. 747–761. Association for Computational Linguistics, Toronto, Canada (Jul 2023). https://doi.org/10.18653/v1/2023.acl-short.65, https://aclanthology.org/2023.acl-short.65
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